OpenRaiser/Intern-Atlas huggingface dataset:
Metrics for Video Quality Assessment in Mobile Scenarios
Venue: arXiv cs.MM 2013
Year: 2013

With exponential increase in the volumes of video traffic in cellular net-works, there is an increasing need for optimizing the quality of video delivery. 4G networks (Long Term Evolution Advanced or LTE A) are being introduced in many countries worldwide, which allow a downlink speed of upto 1 Gbps and uplink of 100 Mbps over a single base station. This makes a strong push towards video broadcasting over LTE networks, characterizing its performance and developing metrics which can be deployed to provide user feedback of video quality and feed-back them to network operators to fine tune the network. In this paper, we characterize the performance of video transmission over LTE A physical layer using popular video quality metrics such as SSIM, Blocking, Blurring, NIQE and BRISQUE. We conduct experiments to find a suitable no-reference metrics for mobile scenario and find that Blocking Metrics is most promising in case of channel or modulation variations but it does not perform well to quantize variations in compression ratios. The metrics BRISQUE is very efficient in quantizing this distortion and performs well in case of network variations also.
Evolutionary Turing in the Context of Evolutionary Machines
Venue: arXiv cs.AI 2013
Year: 2013

One of the roots of evolutionary computation was the idea of Turing about unorganized machines. The goal of this work is the development of foundations for evolutionary computations, connecting Turing's ideas and the contemporary state of art in evolutionary computations. To achieve this goal, we develop a general approach to evolutionary processes in the computational context, building mathematical models of computational systems, functioning of which is based on evolutionary processes, and studying properties of such systems. Operations with evolutionary machines are described and it is explored when definite classes of evolutionary machines are closed with respect to basic operations with these machines. We also study such properties as linguistic and functional equivalence of evolutionary machines and their classes, as well as computational power of evolutionary machines and their classes, comparing of evolutionary machines to conventional automata, such as finite automata or Turing machines.
An Improved ACS Algorithm for the Solutions of Larger TSP Problems
Venue: arXiv cs.AI 2013
Year: 2013

Solving large traveling salesman problem (TSP) in an efficient way is a challenging area for the researchers of computer science. This paper presents a modified version of the ant colony system (ACS) algorithm called Red-Black Ant Colony System (RB-ACS) for the solutions of TSP which is the most prominent member of the combinatorial optimization problem. RB-ACS uses the concept of ant colony system together with the parallel search of genetic algorithm for obtaining the optimal solutions quickly. In this paper, it is shown that the proposed RB-ACS algorithm yields significantly better performance than the existing best-known algorithms.
Making I/O Virtualization Easy with Device Files
Venue: arXiv cs.OS 2013
Year: 2013

Personal computers have diverse and fast-evolving I/O devices, making their I/O virtualization different from that of servers and data centers. In this paper, we present our recent endeavors in simplifying I/O virtualization for personal computers. Our key insight is that many operating systems, including Unix-like ones, abstract I/O devices as device files. There is a small and stable set of operations on device files, therefore, I/O virtualization at the device file boundary requires a one-time effort to support various I/O devices. We present devirtualization, our design of I/O virtualization at the device file boundary and its implementation for Linux/x86 systems. We are able to virtualize various GPUs, input devices, cameras, and audio devices with fewer than 4900 LoC, of which only about 300 are specific to I/O device classes. Our measurements show that devirtualized devices achieve interactive performance indistinguishable from native ones by human users, even when running 3D HD games.
Justificatory and Explanatory Argumentation for Committing Agents
Venue: arXiv cs.AI 2013
Year: 2013

In the interaction between agents we can have an explicative discourse, when communicating preferences or intentions, and a normative discourse, when considering normative knowledge. For justifying their actions our agents are endowed with a Justification and Explanation Logic (JEL), capable to cover both the justification for their commitments and explanations why they had to act in that way, due to the current situation in the environment. Social commitments are used to formalise justificatory and explanatory patterns. The combination of ex- planation, justification, and commitments
Distributed Cognitive Multiple Access Networks: Power Control, Scheduling and Multiuser Diversity
Venue: arXiv cs.IT 2013
Year: 2013

This paper studies optimal distributed power allocation and scheduling policies (DPASPs) for distributed total power and interference limited (DTPIL) cognitive multiple access networks in which secondary users (SU) independently perform power allocation and scheduling tasks using their local knowledge of secondary transmitter secondary base-station (STSB) and secondary transmitter primary base-station (STPB) channel gains. In such networks, transmission powers of SUs are limited by an average total transmission power constraint and by a constraint on the average interference power that SUs cause to the primary base-station. We first establish the joint optimality of water-filling power allocation and threshold-based scheduling policies for DTPIL networks. We then show that the secondary network throughput under the optimal DPASP scales according to $\frac{1}{\e{}n_h}\log\logp{N}$, where $n_h$ is a parameter obtained from the distribution of STSB channel power gains and $N$ is the total number of SUs. From a practical point of view, our results signify the fact that distributed cognitive multiple access networks are capable of harvesting multiuser diversity gains without employing centralized schedulers and feedback links as well as without disrupting primary's quality-of-service (QoS)
Game-theoretic Understanding of Price Dynamics in Mobile Communication Services
Venue: arXiv cs.GT 2013
Year: 2013

In the mobile communication services, users wish to subscribe to high quality service with a low price level, which leads to competition between mobile network operators (MNOs). The MNOs compete with each other by service prices after deciding the extent of investment to improve quality of service (QoS). Unfortunately, the theoretic backgrounds of price dynamics are not known to us, and as a result, effective network planning and regulative actions are hard to make in the competitive market. To explain this competition more detail, we formulate and solve an optimization problem applying the two-stage Cournot and Bertrand competition model. Consequently, we derive a price dynamics that the MNOs increase and decrease their service prices periodically, which completely explains the subsidy dynamics in the real world. Moving forward, to avoid this instability and inefficiency, we suggest a simple regulation rule which leads to a Pareto-optimal equilibrium point. Moreover, we suggest regulator's optimal actions corresponding to user welfare and the regulator's revenue.
Single View Depth Estimation from Examples
Venue: arXiv cs.CV 2013
Year: 2013

We describe a non-parametric, "example-based" method for estimating the depth of an object, viewed in a single photo. Our method consults a database of example 3D geometries, searching for those which look similar to the object in the photo. The known depths of the selected database objects act as shape priors which constrain the process of estimating the object's depth. We show how this process can be performed by optimizing a well defined target likelihood function, via a hard-EM procedure. We address the problem of representing the (possibly infinite) variability of viewing conditions with a finite (and often very small) example set, by proposing an on-the-fly example update scheme. We further demonstrate the importance of non-stationarity in avoiding misleading examples when estimating structured shapes. We evaluate our method and present both qualitative as well as quantitative results for challenging object classes. Finally, we show how this same technique may be readily applied to a number of related problems. These include the novel task of estimating the occluded depth of an object's backside and the task of tailoring custom fitting image-maps for input depths.
Reaching a Consensus in Networks of High-Order Integral Agents under Switching Directed Topology
Venue: arXiv cs.SY 2013
Year: 2013

Consensus problem of high-order integral multi-agent systems under switching directed topology is considered in this study. Depending on whether the agent's full state is available or not, two distributed protocols are proposed to ensure that states of all agents can be convergent to a same stationary value. In the proposed protocols, the gain vector associated with the agent's (estimated) state and the gain vector associated with the relative (estimated) states between agents are designed in a sophisticated way. By this particular design, the high-order integral multi-agent system can be transformed into a first-order integral multi-agent system. And the convergence of the transformed first-order integral agent's state indicates the convergence of the original high-order integral agent's state if and only if all roots of the polynomial, whose coefficients are the entries of the gain vector associated with the relative (estimated) states between agents, are in the open left-half complex plane. Therefore, many analysis techniques in the first-order integral multi-agent system can be directly borrowed to solve the problems in the high-order integral multi-agent system. Due to this property, it is proved that to reach a consensus, the switching directed topology of multi-agent system is only required to be "uniformly jointly quasi-strongly connected", which seems the mildest connectivity condition in the literature. In addition, the consensus problem of discrete-time high-order integral multi-agent systems is studied. The corresponding consensus protocol and performance analysis are presented. Finally, three simulation examples are provided to show the effectiveness of the proposed approach.
Computationally Efficient Robust Beamforming for SINR Balancing in Multicell Downlink
Venue: arXiv cs.IT 2013
Year: 2013

We address the problem of downlink beamformer design for signal-to-interference-plus-noise ratio (SINR) balancing in a multiuser multicell environment with imperfectly estimated channels at base stations (BSs). We first present a semidefinite program (SDP) based approximate solution to the problem. Then, as our main contribution, by exploiting some properties of the robust counterpart of the optimization problem, we arrive at a second-order cone program (SOCP) based approximation of the balancing problem. The advantages of the proposed SOCP-based design are twofold. First, it greatly reduces the computational complexity compared to the SDP-based method. Second, it applies to a wide range of uncertainty models. As a case study, we investigate the performance of proposed formulations when the base station is equipped with a massive antenna array. Numerical experiments are carried out to confirm that the proposed robust designs achieve favorable results in scenarios of practical interest.
A Model for an Intelligent and Adaptive Tutor based on Web by Jackson's Learning Styles Profiler and Expert Systems
Venue: arXiv cs.CY 2013
Year: 2013

Todays, Intelligent and web-based E-Learning is one of the important area in E-Learning. This paper integrates an intelligent and web-based E-Learning with expert system technology to be able to model the learning styles of the learners using Jackson's model. It is intelligent because it can interact with the learners and offer them some subjects in Pedagogy view. Learning process of this system is in the following. First it determines learner's individual characteristics and learning styles based on a questionnaire in Jackson's learning styles profiler. Learning styles profiler is a modern measure of individual differences in learning style. Then learner's model is obtained and an Expert system simulator plans a "pre-test" and rates him. The concept would be presented if the learner scores enough. Subsequently, the system evaluates him by a "post-test". Finally the learner's model would be updated by the modeler based on try-and-error. The proposed system can be available Every Time and Every Where (ETEW) through the web. It improves the learning performance and has some important advantages such as high speed, simplicity of learning, low cost and be available ETEW.
Assessing Visualization Techniques for the Search Process in Digital Libraries
Venue: arXiv cs.DL 2013
Year: 2013

In this paper we present an overview of several visualization techniques to support the search process in Digital Libraries (DLs). The search process typically can be separated into three major phases: query formulation and refinement, browsing through result lists and viewing and interacting with documents and their properties. We discuss a selection of popular visualization techniques that have been developed for the different phases to support the user during the search process. Along prototypes based on the different techniques we show how the approaches have been implemented. Although various visualizations have been developed in prototypical systems very few of these approaches have been adapted into today's DLs. We conclude that this is most likely due to the fact that most systems are not evaluated intensely in real-life scenarios with real information seekers and that results of the interesting visualization techniques are often not comparable. We can say that many of the assessed systems did not properly address the information need of cur-rent users.
A Web-based Adaptive and Intelligent Tutor by Expert Systems
Venue: arXiv cs.CY 2013
Year: 2013

Todays, Intelligent and web-based E-learning is one of regarded topics. So researchers are trying to optimize and expand its application in the field of education. The aim of this paper is developing of E-learning software which is customizable, dynamic, intelligent and adaptive with Pedagogy view for learners in intelligent schools. This system is an integration of adaptive web-based E-learning with expert systems as well. Learning process in this system is as follows. First intelligent tutor determines learning style and characteristics of learner by a questionnaire and then makes his model. After that the expert system simulator plans a pre-test and then calculates his score. If the learner gets the required score, the concept will be trained. Finally the learner will be evaluated by a post-test. The proposed system can improves the education efficiency highly as well as de-creases the costs and problems of an expert tutor. As a result, every time and eve-rywhere (ETEW) learning would be provided via web in this system. Moreover the learners can enjoy a cheap remote learning even at home in a virtual simulated physical class. So they can learn thousands courses very simple and fast.
Distributed Abstraction Algorithm for Online Predicate Detection
Venue: arXiv cs.DC 2013
Year: 2013

Analyzing a distributed computation is a hard problem in general due to the combinatorial explosion in the size of the state-space with the number of processes in the system. By abstracting the computation, unnecessary explorations can be avoided. Computation slicing is an approach for abstracting dis- tributed computations with respect to a given predicate. We focus on regular predicates, a family of predicates that covers a large number of commonly used predicates for runtime verification. The existing algorithms for computation slicing are centralized in nature in which a single process is responsible for computing the slice in either offline or online manner. In this paper, we present a distributed online algorithm for computing the slice of a distributed computation with respect to a regular predicate. Our algorithm distributes the work and storage requirements across the system, thus reducing the space and computation complexities per process. In addition, for conjunctive predicates, our algorithm also reduces the message load per process.
Privacy Preserving Data Mining by Using Implicit Function Theorem
Venue: arXiv cs.CR 2013
Year: 2013

Data mining has made broad significant multidisciplinary field used in vast application domains and extracts knowledge by identifying structural relationship among the objects in large data bases. Privacy preserving data mining is a new area of data mining research for providing privacy of sensitive knowledge of information extracted from data mining system to be shared by the intended persons not to everyone to access. In this paper, we proposed a new approach of privacy preserving data mining by using implicit function theorem for secure transformation of sensitive data obtained from data mining system. we proposed two way enhanced security approach. First transforming original values of sensitive data into different partial derivatives of functional values for perturbation of data. secondly generating symmetric key value by Eigen values of jacobian matrix for secure computation. we given an example of academic sensitive data converting into vector valued functions to explain about our proposed concept and presented implementation based results of new proposed of approach.
An analytic study of a distributed EDCA-based QoS mapping for layered video delivery in WLAN
Venue: arXiv cs.NI 2013
Year: 2013

One of the key challenges in multimedia networks is video delivery over wireless channels. MRC (Multi-Resolution Coding) Layered video, divides video into a base layer and multiple enhancement layers. In this paper, we aim to improve video quality, impacted by high channel contention, through mapping individual video layers to EDCA (Enhanced Distributed Channel Access) access categories in order to maximize the average number of reconstructed video layers. We propose an adaptive cross layer video layers mapping technique that optimally enhances the QoS of wireless video transmission over IEEE 802.11e EDCA priority queues. The optimization is based on a dynamic program that takes into account the EDCA parameters and the layered dependency nature of layered video delivery. Our proposed technique makes use of a channel delay estimation model and an estimation of average video useful layers delivered. The optimal mapping strategies are selected by an optimization module based on the information from the analytical model. The accuracy of our optimized mapping technique performance is verified through extensive simulations. The obtained results illustrate significant trade-off between complexity and delivered video quality for canonical mapping schemes.
Sentiment Analysis : A Literature Survey
Venue: arXiv cs.CL 2013
Year: 2013

Our day-to-day life has always been influenced by what people think. Ideas and opinions of others have always affected our own opinions. The explosion of Web 2.0 has led to increased activity in Podcasting, Blogging, Tagging, Contributing to RSS, Social Bookmarking, and Social Networking. As a result there has been an eruption of interest in people to mine these vast resources of data for opinions. Sentiment Analysis or Opinion Mining is the computational treatment of opinions, sentiments and subjectivity of text. In this report, we take a look at the various challenges and applications of Sentiment Analysis. We will discuss in details various approaches to perform a computational treatment of sentiments and opinions. Various supervised or data-driven techniques to SA like Naïve Byes, Maximum Entropy, SVM, and Voted Perceptrons will be discussed and their strengths and drawbacks will be touched upon. We will also see a new dimension of analyzing sentiments by Cognitive Psychology mainly through the work of Janyce Wiebe, where we will see ways to detect subjectivity, perspective in narrative and understanding the discourse structure. We will also study some specific topics in Sentiment Analysis and the contemporary works in those areas.
Spatial Compressive Sensing for MIMO Radar
Venue: arXiv cs.IT 2013
Year: 2013

We study compressive sensing in the spatial domain to achieve target localization, specifically direction of arrival (DOA), using multiple-input multiple-output (MIMO) radar. A sparse localization framework is proposed for a MIMO array in which transmit and receive elements are placed at random. This allows for a dramatic reduction in the number of elements needed, while still attaining performance comparable to that of a filled (Nyquist) array. By leveraging properties of structured random matrices, we develop a bound on the coherence of the resulting measurement matrix, and obtain conditions under which the measurement matrix satisfies the so-called isotropy property. The coherence and isotropy concepts are used to establish uniform and non-uniform recovery guarantees within the proposed spatial compressive sensing framework. In particular, we show that non-uniform recovery is guaranteed if the product of the number of transmit and receive elements, MN (which is also the number of degrees of freedom), scales with K(log(G))^2, where K is the number of targets and G is proportional to the array aperture and determines the angle resolution. In contrast with a filled virtual MIMO array where the product MN scales linearly with G, the logarithmic dependence on G in the proposed framework supports the high-resolution provided by the virtual array aperture while using a small number of MIMO radar elements. In the numerical results we show that, in the proposed framework, compressive sensing recovery algorithms are capable of better performance than classical methods, such as beamforming and MUSIC.
Optimal Multiuser Zero-Forcing with Per-Antenna Power Constraints for Network MIMO Coordination
Venue: arXiv cs.IT 2013
Year: 2013

We consider a multi-cell multiple-input multiple-output (MIMO) coordinated downlink transmission, also known as network MIMO, under per-antenna power constraints. We investigate a simple multiuser zero-forcing (ZF) linear precoding technique known as block diagonalization (BD) for network MIMO. The optimal form of BD with per-antenna power constraints is proposed. It involves a novel approach of optimizing the precoding matrices over the entire null space of other users' transmissions. An iterative gradient descent method is derived by solving the dual of the throughput maximization problem, which finds the optimal precoding matrices globally and efficiently. The comprehensive simulations illustrate several network MIMO coordination advantages when the optimal BD scheme is used. Its achievable throughput is compared with the capacity region obtained through the recently established duality concept under per-antenna power constraints.